Instructions to use raidavid/whisper-tiny-rai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raidavid/whisper-tiny-rai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="raidavid/whisper-tiny-rai")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("raidavid/whisper-tiny-rai") model = AutoModelForSpeechSeq2Seq.from_pretrained("raidavid/whisper-tiny-rai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97f5dcdd5a0888d334ca4b3f1db6fd832ba31df7b7ff1cb86e32d7b429c67067
- Size of remote file:
- 4.86 kB
- SHA256:
- 8ec071642d3229117358b23996e59d0733c481be83fafe86b3d7523368e3e117
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